Leveraging Transfer Learning to Analyze Opinions, Attitudes, and Behavioral Intentions Toward COVID-19 Vaccines: Social Media Content and Temporal Analysis

Liu, Siru; Li, Jili; Liu, Jialin

doi:10.2196/30251

Cited by 29 publications

(27 citation statements)

References 35 publications

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“…Therefore, it would be common to find a portion of the respondents who got vaccinated although they are against the vaccines. Most of the studies related to people's attitudes towards the vaccines reviewed in the literature were done before obligating citizens to get vaccinated (e.g., Wilson &Wiysonge, 2020;Liu &Liu, 2021;Lueck & Spiers, 2021;Baeza-Rivera et. al., 2021;Matute et.…”

Section: Discussionmentioning

confidence: 99%

“…Siru Liu, Jili Li, and Jialin Liu (2021) analyzed 2,678,372 tweets related to COVID-19 vaccines from 841,978 unique users and annotated 5000 tweets posted over a 3-month period (from November 1, 2020 to January 31, 2021). Results have shown that the prevalence of tweets containing positive behavioral intentions increased over time (Liu, Li, & Liu, 2021).…”

Section: Vaccine-related Content On Social Mediamentioning

confidence: 99%

“…Results have shown that a total of 97 tweets carried positive behavioral intent, while 182 tweets contained negative behavioral intent. Of these, 28 tweets were mapped to capability factors, 155 tweets were related to motivation, 23 tweets were related to opportunities, and 74 tweets did not contain any useful information about the reasons for their behavioral intentions (Liu &Liu, 2021).…”

Section: Impact Of Social Media Health Information On Usersmentioning

confidence: 99%

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Social Media Visuals and Users’ Attitudes Towards COVID-19 Vaccines: A Cluster Analysis Study

El-Sherbini

2021

المجلة العربیة لبحوث الاعلام والاتصال

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Section: Discussionmentioning

confidence: 99%

Section: Vaccine-related Content On Social Mediamentioning

confidence: 99%

Section: Impact Of Social Media Health Information On Usersmentioning

confidence: 99%

See 1 more Smart Citation

Social Media Visuals and Users’ Attitudes Towards COVID-19 Vaccines: A Cluster Analysis Study

El-Sherbini

2021

المجلة العربیة لبحوث الاعلام والاتصال

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“…( 2021 ) 2021 Understanding the public attitudes towards COVID19 Amazon Comprehend ML 1M Tweets Mar20–Jan21 No English TF-IDF K-means clustering N/A Kumaresh ( yyy ) 2021 Analysing public sentiment regarding Covid-19 vaccines VADER ML 1200 Tweets N/A No English TF-IDF Naïve Bayes, LR India Liu et al. ( 2021 ) 2021 Understanding public perceptions of COVID-19 vaccines Manual annotation ML/DL 5000 Tweets Nov20–Jan21 No English TF-IDF BERT, LR, RF, SVM N/A Aygün et al. ( 2021 ) 2021 Analyzing public sentiments related to COVID-19 vaccines Aspect-Based DL 928,402 Tweets Nov20–Mar21 No English, Turkish TF-IDF, Word2Vec BERT 8 Countries Yang and Sornlertlamvanich ( 2021 ) 2021 Investigating Public Perception of COVID-19 Vaccine TextBlob ML 190,000 Tweets Dec20–Jun21 No ...…”

Section: Related Workmentioning

confidence: 99%

Automatically detecting and understanding the perception of COVID-19 vaccination: a middle east case study

Aljedaani

Abuhaimed

Rustam

et al. 2022

Soc. Netw. Anal. Min.

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Introduction The development of COVID-19 vaccines has been a great relief in many countries that have been affected by the pandemic. As a result, many governments have made significant efforts to purchase and administer vaccines to their populations. However, accommodating such vaccines is typically confronted with people’s reluctance and fear. Like any other important event, COVID-19 vaccines have attracted people’s discussions on social media and impacted their opinions about vaccination. Objective The goal of this study is twofold: First, it conducts a sentiment analysis around COVID-19 vaccines by automatically analyzing Arabic users’ tweets. This analysis has been spread over time to better capture the changes in vaccine perceptions. This will provide us with some insights into the most popular and accepted vaccine(s) in the Arab countries, as well as the reasons behind people’s reluctance to take the vaccine. Second, it develops models to detect any vaccine-related tweets, to help with gathering all information related to people’s perception of the virus, and potentially detecting vaccine-related tweets that are not necessarily tagged with the virus’s main hashtags. Methods Arabic Tweets were collected by the authors, starting from January 1st, 2021, until April 20th, 2021. We deployed various Natural Language Processing (NLP) to distill our selected tweets. The curated dataset included in the analysis consisted of 1,098,376 unique tweets. To achieve the first goal, we designed state-of-the-art sentiment analysis techniques to extract knowledge related to the degree of acceptance of all existing vaccines and what are the main obstacles preventing the wide audience from accepting them. To achieve the second goal, we tackle the detection of vaccine-related tweets as a binary classification problem, where various Machine Learning (ML) models were designed to identify such tweets regardless of whether they use the vaccine hashtags or not. Results Generally, we found that the highest positive sentiments were registered for Pfizer-BioNTech, followed by Sinopharm-BIBP and Oxford-AstraZeneca. In addition, we found that 38% of the overall tweets showed negative sentiment, and only 12% had a positive sentiment. It is important to note that the majority of the sentiments vary between neutral and negative, showing the lack of conviction of the importance of vaccination among the large majority of tweeters. This paper extracts the top concerns raised by the tweets and advocates for taking them into account when advertising for the vaccination. Regarding the identification of vaccine-related tweets, the Logistic Regression model scored the highest accuracy of 0.82. Our findings are concluded with implications for public health authorities and the scholarly community to take into account to improve the vaccine’s acceptance.

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“…With the rise in computational power, many researchers were able to apply BERT to COVID-19 and vaccination content in English and test its results against older methods, such as bidirectional long-short term memory, support vector machines, and naïve Bayes. BERT-based architecture proved to be superior both for binary sentiment, relevance, or misinformation classification [ 9 , 13 , 19 , 28 ] and for tertiary stance or sentiment classification [ 14 , 17 , 19 ], which prompted us to choose such architecture for our research.…”

Section: Introductionmentioning

confidence: 99%

Uncovering the Reasons Behind COVID-19 Vaccine Hesitancy in Serbia: Sentiment-Based Topic Modeling

Ljajić¹,

Prodanović²,

Medvecki³

et al. 2022

J Med Internet Res

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Background Since the first COVID-19 vaccine appeared, there has been a growing tendency to automatically determine public attitudes toward it. In particular, it was important to find the reasons for vaccine hesitancy, since it was directly correlated with pandemic protraction. Natural language processing (NLP) and public health researchers have turned to social media (eg, Twitter, Reddit, and Facebook) for user-created content from which they can gauge public opinion on vaccination. To automatically process such content, they use a number of NLP techniques, most notably topic modeling. Topic modeling enables the automatic uncovering and grouping of hidden topics in the text. When applied to content that expresses a negative sentiment toward vaccination, it can give direct insight into the reasons for vaccine hesitancy. Objective This study applies NLP methods to classify vaccination-related tweets by sentiment polarity and uncover the reasons for vaccine hesitancy among the negative tweets in the Serbian language. Methods To study the attitudes and beliefs behind vaccine hesitancy, we collected 2 batches of tweets that mention some aspects of COVID-19 vaccination. The first batch of 8817 tweets was manually annotated as either relevant or irrelevant regarding the COVID-19 vaccination sentiment, and then the relevant tweets were annotated as positive, negative, or neutral. We used the annotated tweets to train a sequential bidirectional encoder representations from transformers (BERT)-based classifier for 2 tweet classification tasks to augment this initial data set. The first classifier distinguished between relevant and irrelevant tweets. The second classifier used the relevant tweets and classified them as negative, positive, or neutral. This sequential classifier was used to annotate the second batch of tweets. The combined data sets resulted in 3286 tweets with a negative sentiment: 1770 (53.9%) from the manually annotated data set and 1516 (46.1%) as a result of automatic classification. Topic modeling methods (latent Dirichlet allocation [LDA] and nonnegative matrix factorization [NMF]) were applied using the 3286 preprocessed tweets to detect the reasons for vaccine hesitancy. Results The relevance classifier achieved an F-score of 0.91 and 0.96 for relevant and irrelevant tweets, respectively. The sentiment polarity classifier achieved an F-score of 0.87, 0.85, and 0.85 for negative, neutral, and positive sentiments, respectively. By summarizing the topics obtained in both models, we extracted 5 main groups of reasons for vaccine hesitancy: concern over vaccine side effects, concern over vaccine effectiveness, concern over insufficiently tested vaccines, mistrust of authorities, and conspiracy theories. Conclusions This paper presents a combination of NLP methods applied to find the reasons for vaccine hesitancy in Serbia. Given these reasons, it is now possible to better understand the concerns of people regarding the vaccination process.

show abstract

Leveraging Transfer Learning to Analyze Opinions, Attitudes, and Behavioral Intentions Toward COVID-19 Vaccines: Social Media Content and Temporal Analysis

Cited by 29 publications

References 35 publications

Social Media Visuals and Users’ Attitudes Towards COVID-19 Vaccines: A Cluster Analysis Study

Social Media Visuals and Users’ Attitudes Towards COVID-19 Vaccines: A Cluster Analysis Study

Automatically detecting and understanding the perception of COVID-19 vaccination: a middle east case study

Uncovering the Reasons Behind COVID-19 Vaccine Hesitancy in Serbia: Sentiment-Based Topic Modeling

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